





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
      import numpy as np
      import torch

      from . import (
          AlbertForPreTraining,
          BartForConditionalGeneration,
          BertForPreTraining,
          BertForQuestionAnswering,
          BertForSequenceClassification,
          CamembertForMaskedLM,
          CTRLLMHeadModel,
          DistilBertForMaskedLM,
          DistilBertForQuestionAnswering,
          DPRContextEncoder,
          DPRQuestionEncoder,
          DPRReader,
          ElectraForPreTraining,
          FlaubertWithLMHeadModel,
          GPTaLMHeadModel,
          LayoutLMForMaskedLM,
          LxmertForPreTraining,
          LxmertVisualFeatureEncoder,
          OpenAIGPTLMHeadModel,
          RobertaForMaskedLM,
          RobertaForSequenceClassification,
          TaForConditionalGeneration,
          TransfoXLLMHeadModel,
          XLMRobertaForMaskedLM,
          XLMWithLMHeadModel,
          XLNetLMHeadModel,
      )


logging.set_verbosity_info()

_lowerCAmelCase								=  {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=False	,_lowerCAmelCase=True  ):
   '''simple docstring'''


   if model_type not in MODEL_CLASSES:
      raise ValueError(f"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()  )}."""  )

   A_   ,					A_   ,					A_   ,					A_  :					List[Any]         =						MODEL_CLASSES[model_type]

   # Initialise TF model
   if config_file in aws_config_map:
      A_  :					List[Any]         =						cached_file(__snake_case	,__snake_case	,force_download=not use_cached_models  )
   A_  :					str         =						config_class.from_json_file(__snake_case  )
   A_  :					Tuple         =						True
   A_  :					List[Any]         =						True
   print(f"""Building TensorFlow model from configuration: {config}"""  )
   A_  :					List[str]         =						model_class(__snake_case  )

   # Load weights from tf checkpoint
   if pytorch_checkpoint_path in aws_config_map.keys():
      A_  :					Union[str, Any]         =						cached_file(
          __snake_case	,__snake_case	,force_download=not use_cached_models  )
   # Load PyTorch checkpoint in tf2 model:
   A_  :					Dict         =						load_pytorch_checkpoint_in_tfa_model(__snake_case	,__snake_case  )

   if compare_with_pt_model:
      A_  :					Tuple         =						tf_model(tf_model.dummy_inputs	,training=__snake_case  )  # build the network

      A_  :					Union[str, Any]         =						torch.load(__snake_case	,map_location="""cpu"""  )
      A_  :					Optional[Any]         =						pt_model_class.from_pretrained(
          pretrained_model_name_or_path=__snake_case	,config=__snake_case	,state_dict=__snake_case  )

      with torch.no_grad():
         A_  :					List[Any]         =						pt_model(**pt_model.dummy_inputs  )

      A_  :					Union[str, Any]         =						pto[0].numpy()
      A_  :					List[Any]         =						tfo[0].numpy()
      A_  :					Optional[Any]         =						np.amax(np.abs(np_pt - np_tf  )  )
      print(f"""Max absolute difference between models outputs {diff}"""  )
      assert diff <= 2e-2, f"""Error, model absolute difference is >2e-2: {diff}"""

   # Save pytorch-model
   print(f"""Save TensorFlow model to {tf_dump_path}"""  )
   tf_model.save_weights(__snake_case	,save_format="""h5"""  )



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=None	,_lowerCAmelCase=None	,_lowerCAmelCase=False	,_lowerCAmelCase=False	,_lowerCAmelCase=False	,_lowerCAmelCase=False	,):
   '''simple docstring'''


   if args_model_type is None:
      A_  :					List[str]         =						list(MODEL_CLASSES.keys()  )
   else:
      A_  :					Optional[int]         =						[args_model_type]

   for j, model_type in enumerate(__snake_case	,start=1  ):
      print("""=""" * 1_0_0  )
      print(f""" Converting model type {j}/{len(__snake_case  )}: {model_type}"""  )
      print("""=""" * 1_0_0  )
      if model_type not in MODEL_CLASSES:
         raise ValueError(f"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()  )}."""  )

      A_   ,					A_   ,					A_   ,					A_   ,					A_  :					int         =						MODEL_CLASSES[model_type]

      if model_shortcut_names_or_path is None:
         A_  :					Optional[Any]         =						list(aws_model_maps.keys()  )
      if config_shortcut_names_or_path is None:
         A_  :					List[str]         =						model_shortcut_names_or_path

      for i, (model_shortcut_name, config_shortcut_name) in enumerate(
          zip(__snake_case	,__snake_case  )	,start=1  ):
         print("""-""" * 1_0_0  )
         if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
            if not only_convert_finetuned_models:
               print(f"""    Skipping finetuned checkpoint {model_shortcut_name}"""  )
               continue
            A_  :					Dict         =						model_shortcut_name
         elif only_convert_finetuned_models:
            print(f"""    Skipping not finetuned checkpoint {model_shortcut_name}"""  )
            continue
         print(
             f"""    Converting checkpoint {i}/{len(__snake_case  )}: {model_shortcut_name} - model_type {model_type}"""  )
         print("""-""" * 1_0_0  )

         if config_shortcut_name in aws_config_map:
            A_  :					str         =						cached_file(__snake_case	,__snake_case	,force_download=not use_cached_models  )
         else:
            A_  :					List[Any]         =						config_shortcut_name

         if model_shortcut_name in aws_model_maps:
            A_  :					List[Any]         =						cached_file(__snake_case	,__snake_case	,force_download=not use_cached_models  )
         else:
            A_  :					Tuple         =						model_shortcut_name

         if os.path.isfile(__snake_case  ):
            A_  :					Any         =						"""converted_model"""

         convert_pt_checkpoint_to_tf(
             model_type=__snake_case	,pytorch_checkpoint_path=__snake_case	,config_file=__snake_case	,tf_dump_path=os.path.join(__snake_case	,model_shortcut_name + """-tf_model.h5"""  )	,compare_with_pt_model=__snake_case	,)
         if remove_cached_files:
            os.remove(__snake_case  )
            os.remove(__snake_case  )


if __name__ == "__main__":
      _lowerCAmelCase								=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
      )
      parser.add_argument(
          """--model_type""",
          default=None,
          type=str,
          help=(
              f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
              """convert all the models from AWS."""
          ),
      )
      parser.add_argument(
          """--pytorch_checkpoint_path""",
          default=None,
          type=str,
          help=(
              """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
              """If not given, will download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          help=(
              """The config json file corresponding to the pre-trained model. \n"""
              """This specifies the model architecture. If not given and """
              """--pytorch_checkpoint_path is not given or is a shortcut name """
              """use the configuration associated to the shortcut name on the AWS"""
          ),
      )
      parser.add_argument(
          """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
      )
      parser.add_argument(
          """--use_cached_models""",
          action="""store_true""",
          help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
      )
      parser.add_argument(
          """--remove_cached_files""",
          action="""store_true""",
          help="""Remove pytorch models after conversion (save memory when converting in batches).""",
      )
      parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
      _lowerCAmelCase								=  parser.parse_args()

      # if args.pytorch_checkpoint_path is not None:
      #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
      #                                 args.pytorch_checkpoint_path,
      #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
      #                                 args.tf_dump_path,
      #                                 compare_with_pt_model=args.compare_with_pt_model,
      #                                 use_cached_models=args.use_cached_models)
      # else:
      convert_all_pt_checkpoints_to_tf(
          args.model_type.lower() if args.model_type is not None else None,
          args.tf_dump_path,
          model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
          if args.pytorch_checkpoint_path is not None
          else None,
          config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
          compare_with_pt_model=args.compare_with_pt_model,
          use_cached_models=args.use_cached_models,
          remove_cached_files=args.remove_cached_files,
          only_convert_finetuned_models=args.only_convert_finetuned_models,
      )














"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
 from transformers.trainer import DEFAULT_CALLBACKS

 from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    []







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	int				,    **UpperCamelCase_ :	int ):





           '''simple docstring'''

           self.events.append('on_init_end' )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           self.events.append('on_train_begin' )







    def 				a__			(		self :	str				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Optional[int]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.events.append('on_train_end' )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           self.events.append('on_epoch_begin' )







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_epoch_end' )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Union[str, Any]				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           self.events.append('on_step_begin' )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Any				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_step_end' )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.events.append('on_evaluate' )







    def 				a__			(		self :	str				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Dict				,    **UpperCamelCase_ :	Any ):





           '''simple docstring'''

           self.events.append('on_predict' )







    def 				a__			(		self :	Any				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	str				,    **UpperCamelCase_ :	Optional[int] ):





           '''simple docstring'''

           self.events.append('on_save' )







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Union[str, Any]				,    **UpperCamelCase_ :	Optional[int] ):





           '''simple docstring'''

           self.events.append('on_log' )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	str				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           self.events.append('on_prediction_step' )



@require_torch
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           shutil.rmtree(self.output_dir )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[int]=0				,    UpperCamelCase_ :	List[Any]=0				,    UpperCamelCase_ :	Optional[int]=6_4				,    UpperCamelCase_ :	Optional[int]=6_4				,    UpperCamelCase_ :	List[Any]=None				,    UpperCamelCase_ :	Optional[Any]=False				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           __magic_name__      				=    RegressionDataset(length=UpperCamelCase_ )
           __magic_name__      				=    RegressionDataset(length=UpperCamelCase_ )
           __magic_name__      				=    RegressionModelConfig(a=UpperCamelCase_				,    b=UpperCamelCase_ )
           __magic_name__      				=    RegressionPreTrainedModel(UpperCamelCase_ )

           __magic_name__      				=    TrainingArguments(self.output_dir				,    disable_tqdm=UpperCamelCase_				,    report_to=[]				,    **UpperCamelCase_ )
           return Trainer(
               UpperCamelCase_				,    UpperCamelCase_				,    train_dataset=UpperCamelCase_				,    eval_dataset=UpperCamelCase_				,    callbacks=UpperCamelCase_				,    )







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           self.assertEqual(len(UpperCamelCase_ )				,    len(UpperCamelCase_ ) )

           # Order doesn't matter
           __magic_name__      				=    sorted(UpperCamelCase_				,    key=lambda UpperCamelCase_ : cb.__name__ if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cb.__class__.__name__ )
           __magic_name__      				=    sorted(UpperCamelCase_				,    key=lambda UpperCamelCase_ : cb.__name__ if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cb.__class__.__name__ )

           for cba, cba in zip(UpperCamelCase_				,    UpperCamelCase_ ):
                  if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )
                  elif isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and not isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(UpperCamelCase_				,    cba.__class__ )
                  elif not isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and isinstance(UpperCamelCase_				,    UpperCamelCase_ ):
                         self.assertEqual(cba.__class__				,    UpperCamelCase_ )
                  else:
                         self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Any ):





           '''simple docstring'''

           __magic_name__      				=    ['on_init_end', 'on_train_begin']
           __magic_name__      				=    0
           __magic_name__      				=    len(trainer.get_eval_dataloader() )
           __magic_name__      				=    ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
           for _ in range(trainer.state.num_train_epochs ):
                  expected_events.append('on_epoch_begin' )
                  for _ in range(UpperCamelCase_ ):
                         step += 1
                         expected_events += ["on_step_begin", "on_step_end"]
                         if step % trainer.args.logging_steps == 0:
                                expected_events.append('on_log' )
                         if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                                expected_events += evaluation_events.copy()
                         if step % trainer.args.save_steps == 0:
                                expected_events.append('on_save' )
                  expected_events.append('on_epoch_end' )
                  if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                         expected_events += evaluation_events.copy()
           expected_events += ["on_log", "on_train_end"]
           return expected_events







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # Callbacks passed at init are added to the default callbacks
           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback] )
           expected_callbacks.append(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
           __magic_name__      				=    self.get_trainer(disable_tqdm=UpperCamelCase_ )
           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [PrinterCallback]
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__      				=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
           __magic_name__      				=    self.get_trainer()

           # We can add, pop, or remove by class name
           trainer.remove_callback(UpperCamelCase_ )
           expected_callbacks.remove(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.pop_callback(UpperCamelCase_ )
           self.assertEqual(cb.__class__				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           trainer.add_callback(UpperCamelCase_ )
           expected_callbacks.insert(0				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           # We can also add, pop, or remove by instance
           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.callback_handler.callbacks[0]
           trainer.remove_callback(UpperCamelCase_ )
           expected_callbacks.remove(UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer()
           __magic_name__      				=    trainer.callback_handler.callbacks[0]
           __magic_name__      				=    trainer.pop_callback(UpperCamelCase_ )
           self.assertEqual(UpperCamelCase_				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )

           trainer.add_callback(UpperCamelCase_ )
           expected_callbacks.insert(0				,    UpperCamelCase_ )
           self.check_callbacks_equality(trainer.callback_handler.callbacks				,    UpperCamelCase_ )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           import warnings

           # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
           warnings.simplefilter(action='ignore'				,    category=UpperCamelCase_ )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback] )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # Independent log/save/eval
           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    logging_steps=5 )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    save_steps=5 )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    eval_steps=5				,    evaluation_strategy='steps' )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           __magic_name__      				=    self.get_trainer(callbacks=[MyTestTrainerCallback]				,    evaluation_strategy='epoch' )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # A bit of everything
           __magic_name__      				=    self.get_trainer(
               callbacks=[MyTestTrainerCallback]				,    logging_steps=3				,    save_steps=1_0				,    eval_steps=5				,    evaluation_strategy='steps'				,    )
           trainer.train()
           __magic_name__      				=    trainer.callback_handler.callbacks[-2].events
           self.assertEqual(UpperCamelCase_				,    self.get_expected_events(UpperCamelCase_ ) )

           # warning should be emitted for duplicated callbacks
           with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
                  __magic_name__      				=    self.get_trainer(
                      callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]				,    )
                  assert str(UpperCamelCase_ ) in warn_mock.call_args[0][0]